{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: NetLogo interaction through the pyNetLogo connector\n",
    "\n",
    "This notebook provides a simple example of interaction between a NetLogo model and the Python environment, using the Wolf Sheep Predation model included in the NetLogo example library (Wilensky, 1999). This model is slightly modified to add additional agent properties and illustrate the exchange of different data types. All files used in the example are available from the pyNetLogo repository at https://github.com/quaquel/pyNetLogo.\n",
    "\n",
    "We start by instantiating a link to NetLogo, loading the model, and executing the `setup` command in NetLogo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "sns.set_context('talk')\n",
    "\n",
    "import pyNetLogo\n",
    "\n",
    "netlogo = pyNetLogo.NetLogoLink(gui=True)\n",
    "\n",
    "netlogo.load_model('./models/Wolf Sheep Predation_v6.nlogo')\n",
    "netlogo.command('setup')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `write_NetLogo_attriblist` method to pass properties to agents from a Pandas dataframe -- for instance, initial values for given attributes. This improves performance by simultaneously setting multiple properties for multiple agents in a single function call.\n",
    "\n",
    "As an example, we first load data from an Excel file into a dataframe. Each row corresponds to an agent, with columns for each attribute (including the `who` NetLogo identifier, which is required). In this case, we set coordinates for the agents using the `xcor` and `ycor` attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>who</th>\n",
       "      <th>xcor</th>\n",
       "      <th>ycor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-24.000000</td>\n",
       "      <td>-24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-23.666667</td>\n",
       "      <td>-23.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-23.333333</td>\n",
       "      <td>-23.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-23.000000</td>\n",
       "      <td>-23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-22.666667</td>\n",
       "      <td>-22.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   who       xcor       ycor\n",
       "0    0 -24.000000 -24.000000\n",
       "1    1 -23.666667 -23.666667\n",
       "2    2 -23.333333 -23.333333\n",
       "3    3 -23.000000 -23.000000\n",
       "4    4 -22.666667 -22.666667"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_xy = pd.read_excel('./data/xy_DataFrame.xlsx')\n",
    "agent_xy[['who','xcor','ycor']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then pass the dataframe to NetLogo, specifying which attributes and which agent type we want to update:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "netlogo.write_NetLogo_attriblist(agent_xy[['who','xcor','ycor']], 'a-sheep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the data exchange by returning data from NetLogo to the Python workspace, using the report method. In the example below, this returns arrays for the `xcor` and `ycor` coordinates of the `sheep` agents, sorted by their `who` number. These are then plotted on a conventional scatter plot.\n",
    "\n",
    "The `report` method directly passes a string to the NetLogo instance, so that the command syntax may need to be adjusted depending on the NetLogo version. The `netlogo_version` property of the link object can be used to check the current version. By default, the link object will use the most recent NetLogo version which was found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if netlogo.netlogo_version == '5':\n",
    "    x = netlogo.report('map [[xcor] of ?1] sort sheep')\n",
    "    y = netlogo.report('map [[ycor] of ?1] sort sheep')\n",
    "else:\n",
    "    x = netlogo.report('map [s -> [xcor] of s] sort sheep')\n",
    "    y = netlogo.report('map [s -> [ycor] of s] sort sheep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "ax.scatter(x, y, s=4)\n",
    "ax.set_xlabel('xcor')\n",
    "ax.set_ylabel('ycor')\n",
    "ax.set_aspect('equal')\n",
    "fig.set_size_inches(5,5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then run the model for 100 ticks and update the Python coordinate arrays for the sheep agents, and return an additional array for each agent's energy value. The latter is plotted on a histogram for each agent type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We can use either of the following commands to run for 100 ticks:\n",
    "\n",
    "netlogo.command('repeat 100 [go]')\n",
    "#netlogo.repeat_command('go', 100)\n",
    "\n",
    "\n",
    "if netlogo.netlogo_version == '5':\n",
    "    x = netlogo.report('map [[xcor] of ?1] sort sheep')\n",
    "    y = netlogo.report('map [[ycor] of ?1] sort sheep')\n",
    "    energy_sheep = netlogo.report('map [[energy] of ?1] sort sheep')\n",
    "else:\n",
    "    #Return sorted arrays so that the x, y and energy properties of each agent are in the same order\n",
    "    x = netlogo.report('map [s -> [xcor] of s] sort sheep')\n",
    "    y = netlogo.report('map [s -> [ycor] of s] sort sheep')\n",
    "    energy_sheep = netlogo.report('map [s -> [energy] of s] sort sheep')\n",
    "     \n",
    "energy_wolves = netlogo.report('[energy] of wolves') #NetLogo returns these in random order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "\n",
    "sc = ax[0].scatter(x, y, s=50, c=energy_sheep,\n",
    "                   cmap=plt.cm.coolwarm)\n",
    "ax[0].set_xlabel('xcor')\n",
    "ax[0].set_ylabel('ycor')\n",
    "ax[0].set_aspect('equal')\n",
    "divider = make_axes_locatable(ax[0])\n",
    "cax = divider.append_axes('right', size='5%', pad=0.1)\n",
    "cbar = plt.colorbar(sc, cax=cax, orientation='vertical')\n",
    "cbar.set_label('Energy of sheep')\n",
    "\n",
    "sns.distplot(energy_sheep, kde=False, bins=10,\n",
    "             ax=ax[1], label='Sheep')\n",
    "sns.distplot(energy_wolves, kde=False, bins=10,\n",
    "             ax=ax[1], label='Wolves')\n",
    "ax[1].set_xlabel('Energy')\n",
    "ax[1].set_ylabel('Counts')\n",
    "ax[1].legend()\n",
    "fig.set_size_inches(14,5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `repeat_report` method returns a Pandas dataframe containing reported values over a given number of ticks, for one or multiple reporters. By default, this assumes the model is run with the \"go\" NetLogo command; this can be set by passing an optional `go` argument. \n",
    "\n",
    "The dataframe is indexed by ticks, with labeled columns for each reporter. In this case, we track the number of wolf and sheep agents over 200 ticks; the outcomes are first plotted as a function of time. The number of wolf agents is then plotted as a function of the number of sheep agents, to approximate a phase-space plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = netlogo.repeat_report(['count wolves','count sheep'], 200, go='go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count wolves</th>\n",
       "      <th>count sheep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>52</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101.0</th>\n",
       "      <td>56</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.0</th>\n",
       "      <td>58</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103.0</th>\n",
       "      <td>57</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104.0</th>\n",
       "      <td>59</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.0</th>\n",
       "      <td>62</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106.0</th>\n",
       "      <td>67</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.0</th>\n",
       "      <td>71</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>72</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109.0</th>\n",
       "      <td>74</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110.0</th>\n",
       "      <td>74</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111.0</th>\n",
       "      <td>77</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112.0</th>\n",
       "      <td>81</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113.0</th>\n",
       "      <td>85</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114.0</th>\n",
       "      <td>91</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115.0</th>\n",
       "      <td>92</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116.0</th>\n",
       "      <td>96</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117.0</th>\n",
       "      <td>101</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118.0</th>\n",
       "      <td>101</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119.0</th>\n",
       "      <td>103</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120.0</th>\n",
       "      <td>106</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121.0</th>\n",
       "      <td>116</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122.0</th>\n",
       "      <td>112</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123.0</th>\n",
       "      <td>117</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124.0</th>\n",
       "      <td>124</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125.0</th>\n",
       "      <td>128</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126.0</th>\n",
       "      <td>131</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127.0</th>\n",
       "      <td>135</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128.0</th>\n",
       "      <td>128</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129.0</th>\n",
       "      <td>127</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270.0</th>\n",
       "      <td>135</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271.0</th>\n",
       "      <td>131</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272.0</th>\n",
       "      <td>125</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273.0</th>\n",
       "      <td>116</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274.0</th>\n",
       "      <td>115</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275.0</th>\n",
       "      <td>115</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276.0</th>\n",
       "      <td>113</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277.0</th>\n",
       "      <td>120</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278.0</th>\n",
       "      <td>118</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279.0</th>\n",
       "      <td>116</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280.0</th>\n",
       "      <td>114</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281.0</th>\n",
       "      <td>116</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282.0</th>\n",
       "      <td>121</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283.0</th>\n",
       "      <td>117</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284.0</th>\n",
       "      <td>111</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285.0</th>\n",
       "      <td>110</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286.0</th>\n",
       "      <td>113</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287.0</th>\n",
       "      <td>113</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288.0</th>\n",
       "      <td>114</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289.0</th>\n",
       "      <td>99</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290.0</th>\n",
       "      <td>92</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291.0</th>\n",
       "      <td>87</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292.0</th>\n",
       "      <td>88</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293.0</th>\n",
       "      <td>92</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294.0</th>\n",
       "      <td>91</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295.0</th>\n",
       "      <td>85</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296.0</th>\n",
       "      <td>86</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297.0</th>\n",
       "      <td>83</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298.0</th>\n",
       "      <td>82</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299.0</th>\n",
       "      <td>77</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       count wolves  count sheep\n",
       "100.0            52          285\n",
       "101.0            56          277\n",
       "102.0            58          266\n",
       "103.0            57          260\n",
       "104.0            59          255\n",
       "105.0            62          248\n",
       "106.0            67          244\n",
       "107.0            71          238\n",
       "108.0            72          224\n",
       "109.0            74          208\n",
       "110.0            74          201\n",
       "111.0            77          198\n",
       "112.0            81          197\n",
       "113.0            85          193\n",
       "114.0            91          187\n",
       "115.0            92          185\n",
       "116.0            96          184\n",
       "117.0           101          187\n",
       "118.0           101          182\n",
       "119.0           103          183\n",
       "120.0           106          183\n",
       "121.0           116          178\n",
       "122.0           112          180\n",
       "123.0           117          182\n",
       "124.0           124          175\n",
       "125.0           128          171\n",
       "126.0           131          170\n",
       "127.0           135          173\n",
       "128.0           128          175\n",
       "129.0           127          176\n",
       "...             ...          ...\n",
       "270.0           135          115\n",
       "271.0           131          110\n",
       "272.0           125          106\n",
       "273.0           116          104\n",
       "274.0           115          101\n",
       "275.0           115          102\n",
       "276.0           113          100\n",
       "277.0           120           98\n",
       "278.0           118          101\n",
       "279.0           116          103\n",
       "280.0           114          104\n",
       "281.0           116          107\n",
       "282.0           121          107\n",
       "283.0           117          107\n",
       "284.0           111          102\n",
       "285.0           110          103\n",
       "286.0           113          108\n",
       "287.0           113          109\n",
       "288.0           114          111\n",
       "289.0            99          109\n",
       "290.0            92          107\n",
       "291.0            87          109\n",
       "292.0            88          109\n",
       "293.0            92          110\n",
       "294.0            91          112\n",
       "295.0            85          114\n",
       "296.0            86          117\n",
       "297.0            83          117\n",
       "298.0            82          113\n",
       "299.0            77          113\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "counts.plot(ax=ax1, use_index=True, legend=True)\n",
    "ax1.set_xlabel('Ticks')\n",
    "ax1.set_ylabel('Counts')\n",
    "\n",
    "ax2.plot(counts['count wolves'], counts['count sheep'])\n",
    "ax2.set_xlabel('Wolves')\n",
    "ax2.set_ylabel('Sheep')\n",
    "fig.set_size_inches(12,5)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `repeat_report` method can also be used with reporters that return a NetLogo list. In this case, the list is converted to a numpy array. As an example, we track the energy of the wolf and sheep agents over 5 ticks, and plot the distribution of the wolves' energy at the final tick recorded in the dataframe.\n",
    "\n",
    "To illustrate different data types, we also track the `[sheep_str] of sheep` reporter (which returns a string property across the sheep agents, converted to a numpy object array), `count sheep` (returning a single numerical variable), and `glob_str` (returning a single string variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_df = netlogo.repeat_report(['[energy] of wolves',\n",
    "                                   '[energy] of sheep',\n",
    "                                   '[sheep_str] of sheep',\n",
    "                                   'count sheep',\n",
    "                                   'glob_str'], 5)\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "sns.distplot(energy_df['[energy] of wolves'].iloc[-1], kde=False, bins=20, ax=ax)\n",
    "ax.set_xlabel('Energy')\n",
    "ax.set_ylabel('Counts')\n",
    "fig.set_size_inches(4,4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>[energy] of wolves</th>\n",
       "      <th>[energy] of sheep</th>\n",
       "      <th>[sheep_str] of sheep</th>\n",
       "      <th>count sheep</th>\n",
       "      <th>glob_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300.0</th>\n",
       "      <td>[41.03368663787842, 18.321898221969604, 11.594...</td>\n",
       "      <td>[21.263946533203125, 28.11968994140625, 29.234...</td>\n",
       "      <td>[sheep, sheep, sheep, sheep, sheep, sheep, she...</td>\n",
       "      <td>116</td>\n",
       "      <td>global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301.0</th>\n",
       "      <td>[31.11406320333481, 15.563537083566189, 5.7447...</td>\n",
       "      <td>[33.33270263671875, 18.54446792602539, 21.2131...</td>\n",
       "      <td>[sheep, sheep, sheep, sheep, sheep, sheep, she...</td>\n",
       "      <td>115</td>\n",
       "      <td>global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302.0</th>\n",
       "      <td>[8.002074167132378, 12.484123229980469, 9.8123...</td>\n",
       "      <td>[26.760986328125, 26.352874755859375, 37.64358...</td>\n",
       "      <td>[sheep, sheep, sheep, sheep, sheep, sheep, she...</td>\n",
       "      <td>118</td>\n",
       "      <td>global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303.0</th>\n",
       "      <td>[0.5315092708915472, 22.81302635371685, 58.086...</td>\n",
       "      <td>[84.6875, 36.643585205078125, 3.51654148101806...</td>\n",
       "      <td>[sheep, sheep, sheep, sheep, sheep, sheep, she...</td>\n",
       "      <td>119</td>\n",
       "      <td>global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304.0</th>\n",
       "      <td>[10.484123229980469, 3.709106147289276, 0.7472...</td>\n",
       "      <td>[17.654922485351562, 20.64666748046875, 23.646...</td>\n",
       "      <td>[sheep, sheep, sheep, sheep, sheep, sheep, she...</td>\n",
       "      <td>120</td>\n",
       "      <td>global</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      [energy] of wolves  \\\n",
       "300.0  [41.03368663787842, 18.321898221969604, 11.594...   \n",
       "301.0  [31.11406320333481, 15.563537083566189, 5.7447...   \n",
       "302.0  [8.002074167132378, 12.484123229980469, 9.8123...   \n",
       "303.0  [0.5315092708915472, 22.81302635371685, 58.086...   \n",
       "304.0  [10.484123229980469, 3.709106147289276, 0.7472...   \n",
       "\n",
       "                                       [energy] of sheep  \\\n",
       "300.0  [21.263946533203125, 28.11968994140625, 29.234...   \n",
       "301.0  [33.33270263671875, 18.54446792602539, 21.2131...   \n",
       "302.0  [26.760986328125, 26.352874755859375, 37.64358...   \n",
       "303.0  [84.6875, 36.643585205078125, 3.51654148101806...   \n",
       "304.0  [17.654922485351562, 20.64666748046875, 23.646...   \n",
       "\n",
       "                                    [sheep_str] of sheep count sheep glob_str  \n",
       "300.0  [sheep, sheep, sheep, sheep, sheep, sheep, she...         116   global  \n",
       "301.0  [sheep, sheep, sheep, sheep, sheep, sheep, she...         115   global  \n",
       "302.0  [sheep, sheep, sheep, sheep, sheep, sheep, she...         118   global  \n",
       "303.0  [sheep, sheep, sheep, sheep, sheep, sheep, she...         119   global  \n",
       "304.0  [sheep, sheep, sheep, sheep, sheep, sheep, she...         120   global  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `patch_report` method can be used to return a dataframe which (for this example) contains the `countdown` attribute of each NetLogo patch. This dataframe essentially replicates the NetLogo environment, with column labels corresponding to the xcor patch coordinates, and indices following the pycor coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countdown_df = netlogo.patch_report('countdown')\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "patches = sns.heatmap(countdown_df, xticklabels=5, yticklabels=5,\n",
    "                      cbar_kws={'label':'countdown'}, ax=ax)\n",
    "ax.set_xlabel('pxcor')\n",
    "ax.set_ylabel('pycor')\n",
    "ax.set_aspect('equal')\n",
    "fig.set_size_inches(8,4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframes can be manipulated with any of the existing Pandas functions, for instance by exporting to an Excel file. The `patch_set` method provides the inverse functionality to `patch_report`, and updates the NetLogo environment from a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "countdown_df.to_excel('countdown.xlsx')\n",
    "netlogo.patch_set('countdown', countdown_df.max()-countdown_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+KTZGdYdlPDm07baEzT1kZoIrZNUF6Vd8Sclv2YTvy6hJ/4sdEyZUddvk0NTzaAmWu7P5UXthUhb4lo8PPx7V7GsUTEJSXSmIamRdjiuJsldjGZUoyuYg4KsoRyebLrhkzDah7BV8FIL0BrCmqn6Xtf0znPc6ZAVVbbMbahhGK7Ow9KUKsmpk3QscnN1RTlUzInIqMF9EfqmqDdE5G+MKI16cNGZrUNYKXlwhmscI5hmVDe6Lc3KEfd1aMUfaMIw2p/Rx8IXUyDoHuB+4U0RuBVbD2eXfAf5V0gk2QVkqeBHphEsLvgRYkHDIBrgGHw+r6viE/YZhLCmU3slaaI2sPXHO1AeBOdHv01W1zTqSlKWCB7bCVY28DFf//eZg/4bAPODTlrpgtn20pmF+bP8RffzGElfevZcnN0zypxI25gDgZ2v4cp3/sFFV5ze/Xvmy/8aG+Hawn3Bz0H98u/ZqB/aInTOj9v88efbbfsfDn7/hF+Hs1iX++jO1/j/RwHn+df+x0dme/NR9fvNriCfuhHbrHlW+P6Eavyk3xPMLwiJwPYL/9aQOMB8u7Z8TNg1J8reEDbG3Xe4XnvzmnG89OUyMgrjPIUyoW3edwZ48e+Hc2Bh18/1t+ZqCfL5evFDY8ruv4Mk9Lnopdkw+wmJ74etN8sGM+e651NcpiBLb4FPUyHoYV5+mbChXBf8x0FdVp4jIIQn7BwDTgLujypKdcKacE9oq3tQwjDZiCazzXihl1/ADQFUnq+qUJg4ZAKyEyyDbDTgR2BZ4QURC975hGJWMNfzISbmu4PNxHFClqo02jLEi8hHwMi5cKTTpGIZRoWTqrel2LtqlglfVWJCxqr4iIjMpMqngZx26Lvr7u/nx2zIwsP3ePPhxTw5tvzX18dXCTiN8uy0zmw74mXTqZrFtmVq/EbXOnejP46L8dUNCu+yInlt48vpfx4tvjb7Lb7Txm/+e5Mlb1k7z5BnHxSPDwuv2GxePFW8uYe2ZpJj3bzsHdvzOvs8hqVBWaHMOm4KE+wshtFNv0nVVT06KpU/LMjXxEMKqnn7z8zmf+GbjfA1iIP5ezqlbypPPnx9veFMylsCVeaG0OwUvIt2AwcDbqvpe1vYqYCnaMKnAMIw2wDo65aTdKXhc9MwVuGyyvbO274HLGhvT+lMyDKPNaGidTNb2SLtT8KpaLyIXAFeIyDXAI8B6wLm4uPgxbTk/wzBaGTPR5KTdKXgAVR0e2duPx1Vz+xH4By6bzDCMJQlzsuak7BW8qo7EVW8Lt98C3NJS17l54uLklkJCcOruCtoRBQ7TYef7hcMAnj7Fb/ay0zz/9ocJOOv+5MsAF1T5lcPeu2S7vNcNk4MemeqPu/uKfurA3PeSkvV86o74oycvmO6PeeKXvWLn5HMahkXOXqvqHjtmh2X8Im5aFxQHDJzb+/4YVrKIO2I3n/qGJ3+wetxPv37gRJ1V33QHo9BhClCX8ZOy36770pOfesJPDntsh/cIyZfYFNK5V9x8UVVd7cmZwEFeCP3GNV2wbMIWP089ZtHYCj4nZa/gDcMwmsRs8DkxBW8YRvvGomhyYgreMIz2ja3gc1KVaaVi+eVOp6VWadEbEXadB/jk+A08OUxaqp/ud7NPaswQFnWqDQpSjV03breeV+t/j4dJKbPn+vJ3C7sSUpPxE2bC5KDNp/h27KQiV2HSTWgf7tjTLzb26bPx1xLOLbSxh4XDwnkWMkYS5668nScPmzTGk8NidNk+nUKpe/R0/xpHxscYPjF9YbC0hEXQfrrohNgx4We1fobvX+j9+GexcxbOnxB3KrUAsy/+Q8H/u91Ov70kcyhXbAVvGEb7xqJocmIK3jCM9o2ZaHJiCt4wjPaNhUnmxBR8iYg3JY43qj75ngM8efx+93ryrBcvj40Rxix3+r/feHKXPlunmifEm1mkjbUGOKnPNv4YdfFWG/lip0NmnrFNbNvmF/n28itX9GPaazIJTccDQn/CVr19f8G9/eMFukZ93rTpNrS5h/cDCrCfBw1gko7P5wsohjAvYP6IMz15/Kh4VfHNpzS/cXeLYSv4nJS9gm+i6fYg4EJgXWAyMEJVr2ibWRqG0WZYmGROyrLhRyNNNN3eIto+Hldw7E7gMhE5JTaIYRiVTUOm8J8ljLJcwRfQdPs8XLng30fyUyLSGThTRK5V1fQ2BsMw2iWZhRZFk4uyVPA00XRbRJYBtsF1L89mNHAasAUQ71jRTKYd6NtpO/byY6k7rruWf0KPeBz8lIvGePJTez3qybs8E9S3SaDDyn6Nj2Js7iGhzX30stvGjskXKx7aiwcHdWWSCG2/h07137awMUUStR38Vdm6V2ziH1Adfx8+PMxv/vz0E6f6101omP7n7st68ol57vs/psYbpoe5EUO7b+TJ1b+7ockxId5kPN89DD+3EM+vCM+ZeYs/5uFXBfcUqJvZ35Orj7rbk1u3Fs2StzIvlHI10TQ23T4XCD1efYHOgAbbGzMrpMRzMwyjnMg0FP6zhFGWK3hVndzE7h7R79pge130O/+yzzCMysFW8DkpSwWfh8Zn1Fzv6pL3NW0YSzAZU/A5aY8KvjHAOlyp1wT7DcNYEmgFJ6uIdMAFfhyNMxNPBh4GhqlqXXTMJsDlwCY4C8PIaH9SoEir0B4V/OdAPdA/2N4oh7b5gsh2RoUOVICqmm6+HDgAXzvjG08eeFH8GmGxrY2YElzDd+5VBY49gOVW38GTB6/8S0/O11SjEAopvpWPsPgYxAuB1dY3/bAVNqZIYmaVP0boyE7io8xKnrz2yOs8ueeIt2LnhA7SfMlhK3WNv3chJ072nZv5HKZJ54TzCh3k9TPiDWBC6m7wE+6qfvF/ntx92/TRx6u8+mlsWzx9rIVonRX8acAFuMCP/wBrAecDvwB2FpH+0fZXgcHAOrg8nRrgmEIvIiJ/Bg4EVgI6JhySUdV+hY7X7hS8qs4TkZeAvUXkKlVtfHf3wa3e32y72RmG0eqUWMGLSBVOwd+oqo0lP58TkWnAPVEy5jE4/bOHqs4HnhCROcC1InKxqub9phWRYcAwYDpuodrscO92p+AjLgCew93ckbjQyFOBoao6p6kTDcOoLFqh5Hk18G/g3mD7+Oh3P2AQ8Gik3BsZDVwf7butgOscCowBdmmpXJ52qeBV9XkR2Qc4F3gIFyt/qpUqMIwlkBQreBGZke8YVe0ZyLXAcQmH7hn9/hhYlcA8rKpTRaSWwkO3ewPnt2SiZtkr+Caabj8IPNhS10lqrtEsjsrf8CFs1Pz+JN9uWdUtbsf94cNRnrxgpP+dNirB9h+Sr2BVrKE4xJqKh4kthTBoun9PQptzaKP/cFi8aUTIyWv4zbDP+aq3L6/h+zkAfjHFL0gW+lfC5t8AtQv8piiDZjf9WpLs5yFh85ZCzgmZPNt/XwYOmJj3nPD9ve/48Z48rvMjeccIC8H1uKj0jUhy0gZRNCKyGTAUt8CcHm0OQ7fBhW8XGro9DmfbbzHKXsEbhmE0RWZh4ZHR4eq8GERkS1wtrC+Bw4FGj3vSN00VhYdunwXcKyJjVPWJ5s4TTMEbhtHeacXMFxEZgrMofALsrKrTRKSxt2TSSr07hYdunwDMAh6NHLTTiL+6yo6iMQzDyKa1Ep1E5CRcnPsYYC9VnQmgqrNEZAJB6LaI9MYp/UJDt7sAn0Y/LYI13Y54aKXfLboRSXHgoc0xbN4R2rF3WckvJAVQ1zDPk2fV+76Ud6d94clzPnk4NkZDHjt9UqGsrmvtEdvW0hRjkw2bdYQx3qGvAOCkmwZ68tx/3OfJYePueV/Go6+r9/R9Xl9f960n37agByFpm12Hrw3ihdGerfcrcrxcQBON0D/wUcZvTL7tit97cph7AVBz+r5NXqP24tH+GGvG14FhrkjYFKRbl3iz83U+faIkDa9nHPCrgpVYz7tfKGoOInIY8E9cJM3BQbQMInIrsB2wduM+ETkKuBZXV8tPlGklbAVvGEb7psQmmmglfg3wNTAC2Ni1qljEZ7jqtwfg4t+vwjlLLwJuKlS5i8g4nG3/ceBVVW12iq4peMMw2jWtYKLZGegKrA6MTdj/e1X9d9Rl7jJc/PsPwHBc4lKhfAYchUuqmiEiz+CU/VOqOrWYiZuCNwyjXZNZWFoFr6p3AHcUcNxYYPNmXGfPqNnRFrgvlUE4h25GRN7EKfsnVDVeSyMHZoOP6LTUKotuRFibAyBTV+fJJ1813ZNnZvx6Qkk1YUK77OB+33lyze+DxgoJTUM6rOXbYLttcqgnJ9ntb9z6Kk8Obd3FUHvpbz254Rs/Hv23o+IJxc++e5Mnh81KCmkoHTazPnvv2Z58wGj/eX3UsLVjY6x43AOeHNaRmTsxvkgL5xr6WPL5VwB+vfTP/HPwn8DDz9C6xGsi7b3U9Ni2bFY70PcfdNxp9yaPB8h85OuL8LNec9pjeccIScol2OS7h0pig/9xj20LVmLLPvxiSeZQKkRkeWAP4HRgTVwUTcELc1vBG4bRrqm0Ph4iUg1sietctzWuOuXSuHDLl9OM1W4VfPQoUwcsE+yarardE04xDKMSqRAFHzlntwY2wHXbm4Gz+Z8BvAi8k1VcsSDarYLH1XdYBvgDLumgEevAaxhLEBW0gm+sd/MdcBVwc2Ot+WJpzwp+AO67e7RVkDSMJZdMyQrNtzq742Lpf4ULu7xERN7Grd5fBF6OCp8VTLt1sorIpcCeqtoixXmyE51qO8Z7kc8MNoVJK2HjiS3mxX05o5ae68kjNv7Rk5fZ1ncIbvP3j2JjrLXU8p58y03be/Iuhz0UO6eQBJpswmYWAM/32sCTN3j5LP+cIpKpspusAHRafYW854SNVj663E8WCouCXbl0/Lt/9GPH+mMGyWHrbfrn2DmzF/rvXd18Xw4dtYUQOt17BCvRmoSGKFLtFxfrWu0nFF33o38Pz3/kkLzz2Pe313ryPYf6jVa2vdEPBgCoDe7H93P8z3LS/Vg4f0JJHJxTti/cydr7P+3DySoiPXHKfjucLX79aNd7qrpJjtNitPcV/E8i8hSwFbAAGAWc0tzHGsMw2g8VZKJZhKrOEJFHgO+BqThrxcZAPEW+Cdq7gq8BbsZljG0CnAOIiPwqrTPCMIx2SqZdLMoLIuoO9evoZ2tcsbJZuHaANwJPphmvPSv4IcCPqvpBJL8kIpNxnVd2AJ5ts5kZhtFqVMoKPmoB2BNXYvhDFiv0sapalKchlQ1eRJZT1WnFXKg1iOxW03HdnS5Pc252olMSW/X27cWX40dihrbf38//MDaG/tGv8hk2mrj1Fr/HbmiTheKaQoRzD5NwQntqaG+GeGOJkBnH+I2aO66b4BoJErcy3/lFvsKEmtlv3hobIrSX17/5lCdX/+4GTw6bUANc1clvApLWR5FEmIDVIxP349Q0+CvN8P19rXO8QFfI0GUDv01QTGzZ0f49W+Pnu8XG+PKd2z059J+E72UhTcivW3qAJx9c93rsnNrZX5RkqT1pq8KLja38cnHFxloDEXkIl636pKrGHR9FkHYF/46I3Kyq57fExYslKv6zO/C8qmaXYOwS/f6h9WdlGEZb0FBftjo7Faq6J4CILBvVnV8dmA98CzybNoIG0iv4FXBG/7amAff4cjVwUtb2Ibg4+FTZXoZhtF8qxUQDi0oMX4ZbrGZ/c80TkVNU9fo046VV8HcCfxKRZ1X1q5Tnthiq+oOIXAccFzW1HYtL7T0TGKGq+Zt5GoZREWQaKmMFLyJ7ANcBb+OU/Me4jNa1cQvZa0XkG1UtuDhQWgXfEF3sUxH5DJhCPHM0o6rbx85seU7GZXwdimt+OwFXmvPSYgabsMXPF/09rzZ+W/qN8+20fw4aJr94lF9IavJFcZt1ki0zLaFNuSbI8hh40WqxcwYMHePJX8ycFDumuYSvbcqu8S5lYTOOpbba0JPDZtBhIbVCCBuZJ+U0tITNPSRtQ5AkQjv+d5l5sWPCz2H25xZg4Zi7PDn0+wBUdfebxHywum8/r5+eP2/w4W7+ddd/4g/+AQPjNvhS0U5TeZIYilPuWwQNRd4VkfuB13ClhEum4HdksX17GSCuTVoJVV2AU+ZFKXTDMCqDSlnB40K/Tw+7RYFBzUQBAAAgAElEQVTTdyLyLyCV/zOVglfVNdMcbxiGUWoqxckK/AR0a2J/NSlrbRUVBy8iHXGJRY1e3m9U9e1ixjIMw2gOFbSCfxH4i4jcpqqeHVVE+gBHk9xRKiepFbyI/Ba4HliFxV7ejIhMBI5W1UfTjlkO9Bq5OGx+4UM3x/Z/cJ0f537UXP8pqpAm0yFhzZdC6pmEDcFDm/Ndw+Kx1O+e6cc1F9PAIWyCETbA+Hw9P9Z+wfR4XkaXP+/pyWFMe/dtT0k9rzAeO/QvHMqkxAbY5ciq9UEeRId4w498hHkAYb0fiL93YXOO+hl+E5Upu/ZnwXRfiS47ergnZ2b58flh7aJSkqmcTNazgNeB8SJyB4ur5K4NHITT12enGTCVgheRrYEHgMm4GsXZXt6jgftFZDtVfTXNuIZRKtqLci9nQuVeblRKmKSqjhORXwHXAn8Jdr8JHKeq76YZM+0K/hzgK2BTVfXCJETkeuAN3LfQb1KOaxiGURQNlbOCR1XfADaPkjnXwFlJvlLVyU2emIO0Cv6XwHmhco8mVisit+BCfQzDMFqFCjLRLEJVp+DC0JtFSxcbywCd8x5lGIbRQrTXKBoR+SL/UTEyqhpPbshBWgX/X+AwEbleVT1PTNQo9nCcmabdkaldXEOtap31Y/vXuHhVT35ypp/IVH1U/uSZ0CEaFvUqpmlEOMbmMxNu/2m+WIxz970NT2py/zE/+K1x9dt4RYv3A6dqVbdlY8ekJWy8EXLi5PTF2dqKsPjYiZPH5D2n5pgdPLnrG9948tJ/Oix+0p2+M3vtwf6F66f7a7Tejyd8tgNH7ZxPHvbk9Z/wk9ZKSTuOovkGtyjOZmNcOOT7wHicj7NvtH0q8HSaC6RV8OcCLwDjRGQEvpf3aOBnQLwdTokQkQNwNv++ON/Axap6R2td3zCMtqe1bfBRzfY3gDWzqz5G2f1Jq+sVVDVWAFFVtwvGHQzcAvxKVV8M9g0EnsA5WwsmnsfdBKo6Ftgb98VwGfBQ9PN3YClgiKq2ypJJRPbD1cZ5BtgTGAPcLiL7tsb1DcMoDzKZqoJ/mouICK5UQKdge3fcQnMoMDD4abrW9mIuBK4OlTuAqr6Ga8R9cpr5prbBq+ojIvI47pFhTSIvL/BWsUXpi+RiYJSqnhjJT4vIsrhU3tGtOA/DMNqQ1qhFIyKdgD8Bl+Dag4ZsgNOFD6vq+CIvszLODJOLWUCvNAMWk+i0Gs4c8/copAcR+Suwn4hcGnl/S4qI9MU9Cp0e7BoNDBaRNVX1yzRjnrD7LYv+3i/BrHtfl/i2bApp+PDgfN8+WkzRr9B+HjbiCItPAbw2vo8nhwW49hjk28tHPL9ibIzaef5nOmzGUUhhsNDmHiY6FUMxfotyISwcd0tH/72c9WK8Z02YDBYmNoVJS0nJY+F7V//8A548bcznOWacmwUjr/DkI26LF0q76+udUo9bCK1kotkKV/fqMlxhwzAbckNgHvBpM67xPnC4iPwzwce5Ai42/r9pBkyb6LQezhTSA7gb1z0J3LfKX4ADRGSrtMq1CNaOfmuwvbFMsAClnoNhGGVAQ+s4WT8G+qrqFBE5JGH/AGAacLeIDMLp1seAE1S10B4aZ+Ps7B+KyF3AF7iijmvhMlmXAvZLM+m0K/hLgDpgoKou+qZS1aEiciPwPM4ePzjluGnpEf0OO5zURb9rSnx9wzDKhDQreBHJaw9X1Z4J2/IlGg0AVsL1Ur0Wtwg9D3hBRDZW1abDvdw1nhOR3+D0bHY+UQZ4CTg5bc2vtAp+c+D8bOWeNbkvo8ia0+KntTiLauDk2F4hycuGYeSjTBKdjgOqVLXRhDJWRD7CdZc7iLhJJxFVfQ7YJDLJrI7TcV8V2ws7rYLvgHtkyEUVi/uilpLGTNpwpV4d7C+YahYXehp4x5ax/YN2uzjtkDHCuijvkj7PIbQ5hzbXzr2mExIWKItxZ7ghHvcc2otDm3vdo7475KtjnoiNEdrc39/qAk/OV9CsvRO+Vxu87OcW7Bs0vz50/3jO4BF9/M/mzRNf8eTNp/h5EGHDdYCGN5715M4H/dWT/33l3zz5pD4rxMY4e2+/IFlVT/9fcdSkePG9u2JbWoY0K/ik1XlLoKr/S9j2iojMxK3u8yIizwMXqup/VHUqgcNVRHYDLlHVdQudV1oF/zpwpIjcqKreo04UJnQ4KZ0ARdJoe+8PfJC1vX+w3zCMCqetGzqJSDecWfptVX0va3sVzm4ei4GP9ncFls/atB3woIgkOWo7ALvgIhcLpphEpxdxiU534pyaDTjFegDOBvXHlGOmRlU/E5EvgX2BB7N27QN8qqrfJJ9pGEalUd+QKp2nFMwDrsAFoOydtX0PnEVjTI7zugHvstinmMHFul+V4/gq4Nkc+xJJ29HpvyKyI3A5cAp+1+/3gEOigPzW4DzgNhGZjvNW7477Ft2/la5vGEYZ0NYON1WtF5ELgCtE5BrgEWA93IL4YVUdk+O8qSJyIK6IYxUuiuZBXLhkSD3OZHNPmrlVZYrMEshyAnTEdXRq+U7O+edwJO6LZlVcSNHFqvqvYsbqtNQq3o0IG0Dfd7yfu3Do1PwJu7ustJEn71fvm//CMabs2p+Q3o9/5slhY42egXVv4st+YxKA2XP9baGdNoydX+XV+BPirSv4/oNw7uFrBRg1fHNPDmO2n+nl25MHTfftyZVO2KwkzGkohPDzcPq06tgxoyb55uGWuO91NxzgydVH3e3JYaw9wNIb7FQSb+hLK+1XsBLb5vv7mj2HKEzyNmDVoFTBYcDxOIvGjzjv1jmFRNBE598G/CPLUdts0sbB34PzlTyZ5ARobVT1RuDGlh43VO7FkKTw0hIq9/ZEqNyNtiFU7pVIQysb4VV1JDAyYfstuFoyxY7b4ubttDb4bXBmkBkicj9O2Y9R1bb2cxiGsYTSQFmESbYIIrIzcCDOn9kx4ZCMqm5f6HhpFfwqOE/v/sBewKHA5Ghlf09SqJBhGEYpyVSIgheRo3FJUuDaoja7DkdaJ2sGVy74hWgyg4AhwO+B46PIlruAO1XVQhUNwyg59RWi4IETcMEquxTboi+kaCdrNiKyFs5jPCTalMHFw/9dVR/OeWIZMf+rNxfdiHp9PX5AXeAAizX88J1Mg1f+ZWyIm/ap9+SeI95qck5JhcO6bewnlEwbM8eTt/gy7uv+11J+XkRtlf+9njcRKoHw9YW23iQ/RuhkDZN2dprX/Hm1FvmapoTOT4B+4/I3hckmvD/gJ+RBvKjdqgt9ZVdTH48xaav7unD+hJJo4qdW3L9gJbbz5HvK9ttARObiate0mF+x6JZ9IvILXOGbwbi6CwuBx3Ge4wxwJPCAiJyjque3wFwNwzBitHWYZAvyORAv5doM0kbRrI1T6IOBxiXKK7hKkqNU9cesw+8VkdeBk3A12g3DMFqcSrHB43pcXC0i96vqhy0xYNoV/EfR7w+AM4C7VPXbJo7/Fli6if2GYRjNov22ZI2xFa6px3siorgw9PABpaRRNJfgHKgfisjywOoishzwpaomFfjaX1XrE7aXHQ2TFif3dFg5bvvO1PzoyfXjHmlyvB5V8UJRPUekSzip3lNiY2zz9488uTZoup2ULDNodtOJLGHCzcPd4q8/TI5Ken3ZPH3MR03uB7jiBL85zejLZuc4MjfFNBDPR9gcHeLNzfM1Wlnl1XT2doj7NcJCYhBPhrvkHb+Zy6mv+B3dugYFzAqhmHsa+lw6rLVpjiNbngoKk9wZZ97+FuiKSyRtFmmjaM4Qka1F5BVgMxaXKqgXkf8Ap2Q/WpRKuYvIWSSbfU5V1XgbHMMwKpZ2sYIsAFVNVUisENLa4LcDngZmA9fh2lN1xHUcORB4VUS2VNVxLTzPkAHAWOK1578u8XUNwygzGqoqZgXf4qQ10VyAa7C9pap6JTBF5DxcOeGLgd1aZHa5GQA8oJoUz2gYxpJEpaTRR/Xg86Kqvy50zLQKfkPgrFC5RxedLCLXA2elHDMVUe3lfiRXXCuafr9Z3Hxi9x7xevphjPZffvIvP+OY//PkniPyF3DKV+SpqjpeOOrdaembhOSjWye/R8vvfvoux5GLSbIPZ1NIrHXNaY81uT9skAJwyax3PLmYAl0hYVOMl6ekt5937tV8NVNI3Zgb3v6ZJw+fNMaX14o32khLaHMPfTQQ/x/Z+tinPXnsyIS+GhuUqOl2SUZtE/oS/77qiKsZvwxucZ3KOpJWwU+m6TjNZYj3SW1pNsAVv99JRC7ClU8YB5yuqk+V+NqGYZQZlRJFo6prJG0XkY642vL/xJVqL5i0lfIvxJUkiJlgRGQzXKrteSnHTEtjgdw+uGSqvXBfPI+LyA4lvrZhGGVGPVUF/7RHVLVeVR/A9XX9e5pz067gBwJTgIdEZDwuLn4+zmSyKa44zgEikl0sOlXcZjYi0oH4l9CjwHfA06q6IDruaVwNh3OB54q5lmEY7ZNKWcEXwKfAsWlOSKvgd8DZiL7BxWlukrWvsU1eS4b6nA0My96gqlXAhGDbAhF5BtcT1jCMJYgKssHnRESWBg7CLbALJm0cfIvHaebhJlw7vkVELQN7qeqo4Ngu5GhuWwgbVy9+aTMzC2L79/3RdyqGSUr10/1EqJag0x5Hx7bN2d7vSJipnebJ3TY5NPV1+iztO8R27Bh3s6zbcS1Pru3oP1gV0uEqTOTJV3yttkPccXlxV7+Ryt5/9F0+87/08+2SmqaEzVie/P6d2DEhcz7xa+aFCUSFNGcJnbnVHZZJPY+aEixX8722JEd2dQ+/6NleS63myVU1y7XQ7PKzBETRLA0I0ItgwZuPoouNtQaqOhGYmL0t6nl4hIiMUdUp0bZuwK6YecYwljgqyESTFEUDLpdrPHA3cH2aActawefgKuBg4EkROR+XTTsU16H8nDacl2EYbUClmGhyRdE0h3an4FX1CxHZGlcX55+4x5eXgMNU9au2nJthGK1PfeWs4IFFYZGb4GrRzAe+UdW3ixmrRRp+VAI/vf/0ohvxyo7xvrk1ned7crcuvty12perV4/b8dM20Q5to1CczT3WfCNoVjL43PH+8Q3zYmOEyT9hEtKJk/Pb4ENCm/Ssej/B5tdL+0k9AEM38ix2RTUmz9espBjiiW7xZi4bLtfXk8desJknh01j2hPh56FHwrL64An/Lokqvn7VgwpWYkd/W5o5tBQi8lucGWYVFtf6yuBM1Uer6qNpxksbB28YhlFWNKT4KWciy8QDOMV+BrAnsDdwJk7J3y8iW6QZs92ZaAzDMLKpIBvEObhyBJuG5dejMjBv4ErB/KbQAU3BG4bRrmntKBoR2RCnbNdU1e+ytg/CZfuvi8uuH6GqV6QY+pfAeUm9NVS1VkRuwQWUFIwp+IjsuN18RcCS+GD1AZ7c7/H0BavCJsvFNGtIpNqPc8985zfhCuOvkxqGh+SzuZ/UZ5vYtuET/UJYD2zq+ynOfMdvtPHQ7E8J+e7tVT25mOYU+ZqVJDH7zVs9ue+2J3lyVU03T6579PT4IEHj9rAJeUiYawHFfTbTMnfiWE/u0mfrvOcU4oM5uOgZNU1rml5ERHC5OZ2C7VtE2+8F/obrznSZiFS1YI+KDJDqw2s2eMMw2jX1KX6KRUQ6icjRuJV7l4RDzgPeVtXfq+pTqnoWcBlwZpSFWgj/BQ6L8nrC61fjMvXfiJ3VBLaCNwyjXdNKJpqtgEtxSnsCrvAXACKyDLANzhmazWhcU6ItgELCzM6NjvtQRK4FPom2rw0cDfwM+HOaSdsK3jCMdk0rRdF8DPRV1XOBhcG+vjjTiQbbG2N4482VE1DVsbiomQ64L5KHop+/A0sBQ1Q1VTyyreAjbtz6qmadf9RcPw7+8/XWiR0zp24pT17/6/c8+bK9/fjz6gfy27ELIrD9ho02wvjsN+f4NnpIHzteyDzDGPa6u3b05A9P8O8pQN+N/Zo/dW/9wpMLqedSTce8x4SE+Qa1l/7Wkztstp0nV3VbNjZGlfj1WWov9e9z+L6EuRetRWaWf49D/wPACbv7uSLhPb3wzQtbfmI5SBNFIyJ5O8SoaqxbiapObuKUHtHvsBdGXfS7prDZAfAuMCr6WQMXMrkG0Bt4OcU4gK3gDcNo5zSQKfinRGQnJCVPsQBEZD3gbeB44CdVHaWq9+KKjB0NvCMiqQo+lvUKXkTuA2aq6uHB9u64x5Z9gO64UgXHq2o87MIwjIomjfM0aXXeAjSGNYYr9Zpgfz4uwa36B2brMlUdKiI3As/j9N7gQidWlit4EakSkUuBfXMcci+wH/BXXPTVKsALItIjx/GGYVQoZZDJ+jnue6Z/sL1RDm3zudgcuCppoaqqXwIjgG3TTKzsFHwUZ/oUrnPJ3IT9W+EyuQ5W1dujVlY7AD1J6WE2DKP901BV+E8pUNV5OCvC3iKSfZV9cKv3NwscqgOur3UuqkgO0cxJOZpobsS9yIE4D3LIINxjzLONG1R1qoi8iFP8qXoWNpKdqDF62fiX5L4/vtjk+WHzhkemxp8EB/f7zpNH1/nX6TnCv0boyAM4+xu/qNXSZ/jO4aSklMEnve7JYZGvsJBYmHAFcOdU/zN66wp+camfLfQdglt/eElsjHwJM1MuGuPJm0+JJ4tNmP5zT64OkpZCh/G7076IjVGMozos2Lb1sU978itP7+3JhRSBC5O0Zp7hO9W/ubPQJ/uWJUywCxukQPy+3zzJT8AanvBeL5w/IbatJSihbT0NF+D6UdwjIiNxoZGnAkNVdU6BY7wOHCkiN6qq5wyOzNKH42LlC6YcFfyxqvoBgFvMx1gb+ExVQ9PbZ8CQEs/NMIwyoxzUu6o+LyL74GLZH8LFyp+aslTBucCLwDgRuROn0xpwpp4DgJWAP6aZV9kp+Ebl3gQ9iIcjgVvVpwlHMgyjAmjtKpGqOhIYmbD9QeDBZoz736gl6eXAKSyOzgF4DzhEVV9LM2abKXgR6UDgA1DVMIEgiSqSv7SrKP+KoIZhtDD1ZbGGbxmiZKfNRGQFXMOPjriGH5OKGa8tV/BnE28gW4gbZCYucyykmsLDkZpk0NDq2LZnLvbt0mHRp82r/ACeE7+PJ5ytO98fY6NV/Abpz2T8/bdcMTs2Ro8G/yHl0BG+rTPJfn7zxHQFqpKODxs6HBoUl3q996aeHCbLJDHrRb8GU/dtT8l7zrxa/yMbJlyFRc7+sOLqsTGKaU4SFgYL/RgNn/glQkL7OsQLoYVyVU//vV3/67ivIPQFhPMK/TZh8lQhhK+tkOSxtqQSV3WqOhWY2txx2lLB34SrvpYWBXaIqrRlf3X3p/BwJMMwKoQycbKWJW2m4FV1Iq4NVVqewRX12YEokiZ6nNkGuKjFJmgYRrvA1Htuys7Jmg9VfUlExuDCkU4DfsR1QpkBNF1g2zCMiqMSTTQtRbtT8BF7A8Nx3uYOuCI8g1V1eksMnmS3DG3MITOr/I9Zkg22tsq/3VOndPevGxSXOnFyftt5aHPeYl7cjXFzIIf29NoO/hroqI39eH2A3o/7dusw3vwUZnnyywnNSqYd6Nt289ncwyYqAP3GvZdw5GLCGPcwXr+lCPMe8jXvAFixm58b8cF2y3tyZkZScFh44aYz7cPPbhhbD9DjoqbzAMLm5+VOJTlZW5qyVvCqukaO7dNx8aCpYkINw6g8zAafm7JW8IZhGPkw9Z4bU/CGYbRrbAWfG1PwEdnxw0nx55sHsdNhvHnYIDqp+XO+ejZhjDO/i7dfPHfl7Tx5p4X+XDf/MX/LxjAOPLQNHz59hbxj3L+y72PoN86vGzPjGL9mDkDPEW81OWbo51j/61TtJxM5ZsareY8J7+mwSWPynhPWYglj+hfed3fsnPD1L7VmP08ObePhmBD3W0zYwq/NE+YJ5LO3J5FUv6ecMSdrbkzBG4bRrsnYCj4npuANw2jXWBRNbkzBG4bRrjETTW5MwRuG0a5pyNgKPhem4COyE0SSkkMO/tpP0pnxnl9M6+aJfrG3pE70mdn+OaHDrHbEc578+Xr+NQH6jRvjbwgchEnJQet/7ScHDV75l57cI3AYDvksXigsTGza8Vu/OX14z5Kce3WPnh5s8Mf48IRCG9/kZs4nD3vyy5tdGjvmu05LefKhk9IXH7vtUb95WIc+vrOzvuaR2DnhZyL8PJx7v1/kLikRLHx9IfOvOc/fMK7Jw4G4k33ybP99Sfocrq++I3by7X6Dk0ISv1oKU++5KWsF30TT7YOAfyWccp2qHtMqkzMMoyywMMnclKWCj/oa/h3XdPuWhEMG4Lqd/D7Y/n2Jp2YYRplhUTS5KTsFHzXdvgZXHTLWdDtiAPCWqr6eY79hGEsIC03B56TsFDz5m26DU/BXt+RFs23MnQ85Oba/R0LxrKZIaro8Zdf+nhwm2Dz9qV8o7NCpcdtwWDxryKP+Q0xVzXKxcz7fe6gn9xvnN8kImyr36RhvePLY9MDmeo3fZLr/KU96cpiAA3Dfof73cfj66m44wD/hqPSJTpnaaZ4cNmZJ4pleTTdzSSJfU+2kxu3bvOdXs+7y5/08+aSbBnrysN3GxMYIX9/CB0Z6cr5kMognUIVjVu92sSeHSWxJ9D/CT+zq22PlvOe0FLaCz005Kvgmm26LyMpAb2AjERkP9AO+AC5Q1SS7vGEYFYyFSeamQ/5DWpcCmm43hon0BU4DdgXeAO4QEasuaRhLGJlMpuCfJY322HT7TWA34EVVrYu2PSMiKwLnA7e16EQNwyhrLIomN+2u6baq/kByL9fHcb1al4+OSUV2gaXph8Tjj0P7cKauzpM7H/RXT+7Sx2+GDdD78c88eeYZfTw5jB1PsuPu8qRvcw9jqRsm+UXPAJap8b83w6bK9xzq29z3v9V/bZA+znmVV2fEtn2weldPjjXqDu5pMc0qQtt4IXkBhdjcw2YlHXv5r6Wqppsn//CI31AdoMtvg6YxQR5AeE/DZi4Qj7df+bL/Jk84IikfI7xHSXHu+cjX/PuZXuumHrNYrFRBbtpd020RGQj8QlXD8MkuwEJgZgvMzTCMdkJrrOBFpBNQhwsAyWa2qnZPOKUsaI9NtwcCV4jIG6r6Piwy9+wLvKKqC1pwmoZhlDmtZFsXnHL/A/BJ1vb61rh4sZRjFE0+bgOOAx4UkbNw36pHA+vhYucNw1iCaKUomgHRpUar6pzWuWTzqSpnz7KIfAU8l1CqYHXgEuBXQA3O8XqGqr5c7LWOWmPwohtx2d7zYvvrp/vv6QtP9vbkgWv7DyNh4wXIH0+c3XSkkU57HuHJy6872JOrl+riydvWrBUb45Yr/OYbme++9eQhwyd4cl1D/PW/PCV/LHQ2SfbzTK3fnOR/d/pPu4XYwkPC2ixdE/IV8tmLQwqx24cNX6565DBPbvgkHsOftj5LIfbzfHVkCmHuxLGe3DAx7sfJF/cf+iiW2n/P2DFddjomr4+tGAatunPBSuyZb58qag4icimwp6rG/8HKmLJewTfRdPtr4ICkfZVEqNyN9MS6ZBmpyafc25pWiqIZAPwkIk8BWwELgFHAKVnRfGVHWSt4wzCMfNRnCjfSiEjeRxxV7ZmweQDOWnAzcBGwCXCOG1J+paplaQoxBW8YRrumlUoVDAF+zErEfElEJgP/BnYAnm2NSaTFFLxhGO2aNA0/cqzOCznvxYTNj0e/B2AKvry5eeJiB98Vq8WdnR23WNWTn37hfU/e99W4YyofXTsv7cnZTUcA6n7mXxNgpa7LevIXM/1GI3XV8SjR0LkXOsSqq/xEpyenvBMbI2z4kZ0YBvkTwQDOHenLw6f7SUthMbYF0+P+sNB5neRU9ZiZ3ukYOlSTyP68AFxdc5p/QHVcj4TO29lz/cYj6903xJMLsX136+Q72etu2MWfxlF+EbAkwqS8sPhaUuG48H1Y7k7fCV+363Z5r9tSlHr9LiK9gd2B51W9TieNNz91YmVrUXa1aAzDMNLQQKbgn6Iv4archs2EhuDi4IuO3is1toI3DKNdU+ooGlX9QUSuA44TkVpgLLAlcCYwQlU/a3KANsQUvGEY7Zo0UTTN4GTgO+BQYCgwAVdLK970t4wo60Sn1mTuLac0fSN6+DbVp4/5yJNPq//Ek2cvjDejqpvvb5uz4CdPDpNWft4t3jThyVv8BJKwwUdVN99GD9B325M8OV8yTFKS0kbX+T6G0PZfCsLmJhBvEjLjGD+Jq5CGF0kJZdnMe3F8bNtSa/bw5I47DfLk8H0opGH2E5sP9+R9f/T9eKFPAuIF6/L5RpIIm5/Pv8fvqxPa0wsh9MEk2f4Xzp9QkkSnTftsU7ASe2PiSyWZQ7lSdit4EemPizPdCugGfAD8TVVfyDqmE+7b8xBgOeAt4GRV/V9sQMMwKhpbpOamrJysIrIsMAZYGzgB58SYAjwnIltlHXo1cBKuMfcQXBXJ50TEX84YhlHxtIKTtd1Sbiv4g3Ht+DZT1QkAIvIs8B5wCvCyiKwBHAkco6r/iI55Blfh7VTActMNYwnCVvC5KTcF/x0wvFG5A6hqvYh8CjQaJH8NdATuzzrmJxF5DGjauNoE+eKFQ7vlDr+b5cnH3re2J9d2iH/ohk0a48mhjXlm8DyVNMaAg0Z6cmjrD+38AD98OMqT88WOr3X1+7FtR9ds6MnX57nub5ePF+waNSmdBS2p6XjYqLzniDFNjpFkbw/zDQohzB3oGTSmvnLFXzUpA9yz5VWefGhgcw/jzx/7nx8nDzBhC9+EvEqQfxH6T5IapIT+grB5SSFjhLHxSXkPrUW9dWXNSVkpeFUdDYzO3iYivYBtgWeiTWsD01V1anD6Z8BqItJFVeNazjCMiiRNJuuSRlkp+JCokcfNQDVwWbS5B1CbcHjjEqIaMAVvGEsIrVSLpl1Stk23RaQzMBLYB2dvb4x/qyI5O7nx2dWe1wxjCcJW8Lkpy6bbIkzDJikAABOhSURBVNITeABnmjlWVa/LOmYmrmxnSGNBlaTVfV7CBtc7P7ibL+/nt4B9eEf//AM3ibeCDWOWw2uM6uQ3wEiyUYeNl7fv5gcKzcz4tWfenOM384AC6rUEJMXJn3S5H5N/Ug/fJr3LCWM8eWJ9XeomIWHjCYDMLL+p+FnbXZFqzJrTHovFyhfDUc/79XrChh+D1/jOk0PbeBLhe3tfrPvbfIb/bTVvS/VRcb9ENmHD9F1W2oh/b+83cAlj9MPGIjft+i9PvnLFX3HYyX5T8Zgfo4haTC2FreBzU3ZNt0WkD64yWz9gf1W9LzhEgWVFpJeqTs/a3h/4UlXnN3dioXIvhlC5F0OoANoTaZV7EqFyL4aWUO5tRajciyFU7sUQKvdyw1bwuSmrptsi0h14DlgF2FFV40u6xWU598XZ5xGRpYFdgadLNmHDMMqSVipV0C4pNyfrMGAdXKeUBSKyeda+ear6rqp+LSK3A9dEXwif4pKeelHmdSEMw2h5zESTm3JT8PtEv8+JfrL5nMWx8EcC03FFf7rjShXsWM5V3QzDKA0ZW8HnxIqNRcz84w6LbsQyJ54a2z/jOD+xJWx4MHVKd09etX/cUXnOV72bnEPYRKIQwoSTQpx7pSB0IIeFs1qKtMXF+vaIF2x75y/+PQsTeV7vvWnsnM2nvOHJ4eut7ehnqf1sYdwVNGi6//6GDV/C4nNhUhfAKY8e7MmZ2b6f4tD9/aS2HlWdY2NcPcZvTnLZtld6cpiQFxYSg/yJgUm+j+7DHylJoa/Vl9ugYCX29bT3rdiYYRhGe8EWqbkxBW8YRrtmSSwiViim4A3DaNfUN5gNPhem4CPueG6lRX8fOWy52P6fv/GNJ/+3j19crFsX3+Ya2ugBfpbx7aEfMseTd1lpI09+8vt48+uwcfOyo0d4clKNhrCp8la9/SSlYmLWZ714uSeHyTOF2LGL4U/3d/Tk0I79/YV+BlpVtZ+gBPkLY6171Sbxjb/z516MjyFs6PLc8qt7cv/X/PfyzE3OjI0xe9hFnvzuqyt68icd/P7PtQmNZ8LEt7Ag2wkz/PyL+g/9ZjaFkOQbWTg84cAWwKJocmMK3jCMdo3Z4HNjCt4wjHaN2eBzYwreMIx2ja3gc2Nx8BELfvhi0Y1Yd53Bsf1hY42wIFdoC/4goXvgnDq/gcPsub58Cn4TkWJs42FjZ4jbXAux9ZeC0H/Qtdr3W/Qb57/epKbTf3jLt6mHcw9jtuc+Frf7dznQt9NX/+6GHDMunLD5dZLtuyUalYe+j0ztNE9++g+vevKOf47/f4cNXfI1YW8pStV0u1f3/gUrsemzPrM4eMMwjPaCmWhyU3YKXkT6AxcBWwHdgA+Av6nqC1nHbAUkFSJ7XFWLbttnGEb7w6wQuSkrBS8iywJjgB+BE3C13f8EPCci26rqy9GhA4DZwA7BENMxDGOJwsoF56asFDxwMNAb2Kyx8baIPAu8B5wCZCv4car6epvM0jCMsqG14uBF5ADgLKAv8BVwsare0SoXL5JyU/DfAcMblTuAqtaLyKcsriQJsCHwbkte+LhNhi76u6ZTl9j+0EEWFrEK9784eSVCdvq53/Vn/Vff8+TQUZdEvgSjpO5NYfehsKhZmIBTiNMtnEeHlf0CXknz2GzieE8Oi2uFhdMWJDyPhU7VMGkrXxEsgLo/7+fJ4WtJ4ubBj3vy/ZnvPXniT/49C53yxRDeD4gXEwu7gIVFvjqsulZsjLr5/2323PLRms1qWmMFLyL7AXcCVwNPAXsCt4vIHFUdXfIJFElZKfjoRnk3S0R64Vr3PRPJHYD1gB9E5O3o7+9xN364qtrzmmEsQTS0Trngi4FRqnpiJD8dmZTPJ9BZ5USH/Ie0HZEyvxnXb/WyaPNaQBdAgL8DOwMPRvvPaf1ZGobRlmQymYJ/ikFE+uJaiN4f7BoNrC0iazbzJZSMNlvBR8rb+4JR1YVZ+zsDI3FNQI5R1cbiFhOAXYB3VbXxOfl5EekK/FVELlfVpouNGIZRMaRR3CKS1/6oqj2DTY2FpzTY3thgSIAvC55EK9KWJpqzcS36sqkCEJGewAM408yxqnpd4wGR8n4qYbzHgcNxN/vNtJO54atRrZ4AsTD/IfnHmB9vxpCP5qf15Gfh/An5Dypm3JKMmp9jv/Xv87FtNI+78siFUHtYfp9De2JBigSqQhR8Aj2i37XB9saFZE0RY7YKbangbwIeCzeKSB9cY+1+wP6qel+wf31cjPw/VXVB1q5Gz6hfTs8wDCMiYXVeCI1fIOGjQuP2sq1X3GYKXlUnAhOzt0VNtJ8DVsH1WE1KZvo5cD3OVPNI1vYhuMekr0syYcMwllRmRr/DlXp1sL/sKKsoGpzJZh2cs3SBiGyetW+eqr6LW/W/CdwsIr2Bb4EDgd2BfSyKxjCMFqbR9t4fl1lPlpy9v+wotyiafaLf5wCvBT+jAVR1Ps7J+hDuC+Fh4BfAXqr6YCvP1zCMCkdVP8NZB/YNdu0DfKqq38TPKg+smqRhGEYeROQQ4DbgOpwVYXfgKJyf8N42nFqTmII3DMMoABE5ElcyZVXgC1ypgn+17ayaxhS8YRhGhVJuNnjDMAyjhTAFbxiGUaGYgjcMw6hQTMEbhmFUKKbgDcMwKpRyy2RtNUrV+7XAcTvhkrQOAZYD3gJOVtX/xQaMj38fMFNVDw+2HwQkhWxdp6rHFDlmd1xJ5n2A7sBLwPGq+mm+eWaNcRauZnbIqaqauupVKbrqRO9HHbBMsGu2qnYvYrwNgTeANVX1u6ztg4ALgXWBycAIVb2imWN+hqvbFLKCqsbqMkVVXP8EHI27h5NxyYLDGquwisgmwOXAJrgCWyOj/QvC8VKM+RywfcLpm6pq6uKARmEskQq+VL1fU4x7NU65/xVXO+ek6JgNVfWLHGNX4ZTtvsAtCYcMwJUv/X2w/fuEYwsd815gU+BUnAIcBrwgIuuqaqH1NwbgviRPC7anrhlUwq46glPufwA+ydpeX8QcBZcI0ynYvkW0/V7gb7gFwGUiUpXvi66JMbvjFOpQ4MXgtFxVE08DLsD1T/gPrr/C+bhs8J2jBcp/gFeBwbjSIRfi6rDkWig0OWZ0zADc+3ZPcO7HOcY0WoAlUsFTut6veccVkTWAI3E17v8RHfMMTrGcisuO84j+wa8BtgFy9YIbALxV6FzzjRk9vfwG2EVVn4q2jcWlbP8Z98VQCAOAB1qof26puuoMwFUEHK2qc4oZIHoK+BNwCZC00j0PeFtVG7+An4p6HpwpIteq6k/hCQWMuQGuouHDqjo+YX84XhVOGd+oqqdHm58TkWnAPdFTwjG44ll7RGVBnhCROcC1InJxdjvNFGNOAZYHnrI+yq3LkmqDT+z9CnyK/7i7IfB+C4/7a6AjWd1hon/ux3AKNYkbcTWpB+L+WZIYkHKu+cYchFu1P5s1z6m4lWKueXqISDfc604zr1xjlbKrzobA58Uq94itgEuBK3BPZosQkWVwX6RJc+8JbJF2zIgNgXm4z1chVAP/Jl5GvvHLoR/ufX80Uu7Z8+wY7StmzA2jv5v9OTDSsUSu4EvV+7WQcXHdYaZHyjKbz4DVRKSLqoYr6mNV9YNovNjrEZGVcU8OG4nIeNw/1RfABU2kUjc5ZjTPz6IvqHCeQ3KMGbIBbhGxk4hchCsDPQ44vfGpIAWl7KozAPhJRJ7CKdUFwCjglBTdwT4G+qrqlKhuSTZ9gc40PfcXiNPUmI3zngbcHdn3O+EWCidkdTtbhKrWAscljLNn1vVWDeepqlNFpDaaZ9oxPwT2An4CzhORPXH+nOejeX6ScK7RQiypK3iPUvV+zTFuD+KdYWBxd5jqcEejIm6CAdHvvrjH5V1xDrk7ROSPSScUMGZT8yy0g03jvPrgzFJ74Rxwj4tI6NfIRym76gzAfSk+gXs6OR84AHg0MkHkRVUnq2qup6ui5p5nzMZ5r4RTorsBJ+IWEy+ISJcmzluEiGyGs+E/xGLfUrPe9+wxI9PRAGBpnClwb+AwXKndsSKyUiFjGsVR8Sv4EvZ+vQLwHukLHLeKeGeYxu0AVZHtNTZmE7yJ+wd/MWvF+YyIrAicLyLeKr7AMZuaZ6yDTdJ9Bh7Fma2ebozAEJGncT6Jc3HNXQqllF11hgA/Zn3pvSQik3Gmhx3IMlMVSa65N1Ls3I8DqlT1v5E8VkQ+wvmQDsItLnIiIlviVvxf4tpdLt3EPBPf9wLGBOeAvTGIJHsN98RwLHBmvnGN4qh4BU/per9eDhyRdlycAytpJdS4cg8frfOuIKNwuFj7w2iuOxB30BWyKp2JeyIIqSa5g03sPqtqFe6LMnvbgsip7IVkFjgfKEFXHVUNI1DA3Ttwq8/mKvhcc68J9qciKaxWVV8RkZksfnpKRESG4BYgnwA7q+q0KConaZ7gzCpNzjNpzGhO4xLm+YWIfJxvnkbzWBIUfKl6v/4zGjvVuDj75rIi0ktVs8Mt+wPfsLjpScGIyEDgF6oahjp2wfWp3or0IX8K7BCF8WWv6PqT3MEmdp9FZEegl6qOSphX2t65JemqE3UF2x14PghRbckev5/j7n//YHvRc48c2INxkTnvZW2vApaiiXmLyEm4BcoYXKOcmQCqOktEJoTzjO5RTVPzzDVmNJ+DgK8SWnAW8zkwUlDxCr6EvV/fCB2tBY7buBrcl+gRWkSWxtnNny4y6WMgcIWIvKGq70djdoiu8UrWI3wansE9Oi8yUYjICrhokIvCg3Pc52uAI0RkTKMtOVJMu5LOPIOqfiYijV11sjt3NberTgMuouhqXD5CI0NwSvnlpJPSoKrzROQlYG8RuSrrc7MPblVczHs+DxddMwZn125kD5ziHJN0kogcFp13L3BwEC0D7n3fTUROzdq3D+5epB5TVTMiciowX0R+qaoN0Tkb475ILi7w9RpFUPEKPgel6v2ad1xV/VpEbgeuib4QPsUpll64kLhiuA1nj31QXOZoHS6rcD2cQk6Nqr4kImNwscyn4ZK3zsEl0NxQ4DBX4XIDnhSR83GmoaG4DN9zipjWecBtIjKdxV11BgP7FzEW4MxbInIdcFwUKTIW2BL35TZCXbu2luAC3JfaPSIyEhcaeSowtJjwTFWtF5ELcF/s1+AWIevhfBsPq+qY8Jzoc3wNLslsBLBxEEH1Ge4zeAAu/v0qXLDBRcBNSV+iBY55Di5E9E4RuRVYLbof75CcfW20EEtqFE2per/mHTfiSOAfOGV3L+6LdsdilUlk6tkW+B9wJS7ErzuwfZGr90b2ximOy3G21e+iMRMzeRPm9QWwNc6v8E/gDtwj+daq+lXayajqSFyS1U6492U73IqxuS3TTgbOwH1RPI7LaB2Gv6JvFqr6PO7zsQ5u7gfiyjUU+6WOqg7H+TK2w71Pp+A+VwfkOGVnoCuwOu6LLPyM7hxFvQzCfX5G4+7BcOD4Zoz5AC5ssh/u6eviaL6DEsJwjRbEOjoZhmFUKEvqCt4wDKPiMQVvGIZRoZiCNwzDqFBMwRuGYVQopuANwzAqFFPwhmEYFYopeMMwjArFFLxhGEaFYgreMAyjQjEFbxiGUaEsqcXGjFZERL7CFdp6DVfEa0XgXeAsVX1BRHYGngTuU9XBWefdhKu5n934ezNcnZiBuEqQr+MKdn2Qdd7W0TGNxd7+B5yjqi8Fc3oWt8g5EFcjZ6OEVoqG0W6xFbzRWuwIXIcrYPU3XA/Zp0Vk20h53w7sJyI7waJa8kcA/8hS7lsDL+GKvl2Gq0i4LjBGRNaIjtkdV9Z2NVzrvfOjv/8T7cvmAFxD6OOBm025G5WGFRszSk60Wl4dV4nzoWjbCrjOP+NVdWDUnPxDYDZu5f02rhPVAFWdHZ3z/+3dP2gUURDH8S/YWAUhkAgKyUXMpFBQMVjEwuvstRGsRfxTCWKlYCOCdpZiI9jZ2NloJyoE0UKdxJADwQQLTXeVYjHzwnocgRR3R15+n+bYx9zbXTiGx95jf++IUOijJS3IzGaJ6LeHxBshV4nIuSMZCF0StkqqUCtTpTo516y7rwzy/kVGRSt4GZavpbkD5Gr5KXDKzCbyFcSXiRCI98BB4lXApblPAPPAs9Lcc54l4CQRjH4iv/eoNPes2SDeVX4ga4tvau5SMzV4GZbPfcaWiRCQKQB3f0Fk2c4QIc1vGrVTWbvcO4m7f8im3ypDfc71pTFP8XM7NyCy06jBy7D0RsMB7MnPP7AZ53c8x87mcW/t3y3OsVWYePmtN69DYRNSNTV4GZZDfcYOE012NY/vAdNElN00/+d1lri43uBqzOy+md0COjk01+dcJUfu+zauWWRHU4OXYZlvZtSa2SRwEXjl7r/NbAG4SmR/PgCeANfM7DRshnp/BC6Y2VhjnhaxC2YSWATWgCs9NWNERu1a1ojsCtpFIwOXO1b2A10iM7ZLNPNxIuB6idgXvw+Yc/cNMxsnnqX/InbSdM2sDbwkVvyPicc114G9wDF3Xzezc0Qm7UrWQOSWzgDnG7t4OkDH3c8M8NZFRkoreBmWt0TI+CXgNvGn64K7fyJCyg24kTteyD9NbxKPce7m2GugTYR/38n5FnOe9ax5ToRG/8iasnWy3dzFI7IbaAUvA6fVsshoaAUvIlIpNXgRkUqpwYuIVErP4EVEKqUVvIhIpdTgRUQqpQYvIlIpNXgRkUqpwYuIVEoNXkSkUv8AVk42SFMjLoIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countdown_update_df = netlogo.patch_report('countdown')\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "patches = sns.heatmap(countdown_update_df, xticklabels=5, yticklabels=5, cbar_kws={'label':'countdown'}, ax=ax)\n",
    "ax.set_xlabel('pxcor')\n",
    "ax.set_ylabel('pycor')\n",
    "ax.set_aspect('equal')\n",
    "fig.set_size_inches(8,4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the `kill_workspace()` method shuts down the NetLogo instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "netlogo.kill_workspace()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
